Data collection methods, systems and computer program products are widely used to collect data about a plurality of samples. Data collection methods, systems and computer program products also may be used to schedule times for collection of the data about the plurality of samples. The data that is collected may be stored in a database, and may be processed to attain useful results.
Data collection is widely used, for example, in conducting research with living organisms, such as plants, animals (including human beings), prokayotes, fungi, protists, viruses and prions. In conducting such research, a large number of samples may be used, and data about characteristics of the samples may be collected at various times, to measure changes in characteristics of the samples over time.
A specific example of a plant research environment now will be described. However, similar environments may be found in research environments for other organisms.
In a plant research environment, studies may be made as to gene function in plants, plant growth and maintenance, mutant generation and/or phenomic measurements over a large number of samples, by measuring characteristics of the samples that change over time. A large number of samples, up to hundreds of thousands or more samples, may undergo testing simultaneously. Plants may be studied as they grow in various media, such as in soil or other plates, in very large volumes and at locations that may be spread over different facilities.
It may be difficult to effectively collect data about these samples. In particular, it may be difficult to collect this data in a time-critical matter. Since plant research may measure growing systems that are evolving over time, it may be important to make these measurements at predetermined time intervals. Moreover, because the data is being collected for living organisms, it may be difficult to determine in advance what characteristics are to be measured at what particular time. Finally, many characteristics may need to be recorded, such as color, shape or other attributes of plants. Although some of these measurements may be automated, many of these measurements may need to be done by visual observation and recording, which may be time-consuming and error-prone.
FIG. 1 illustrates a conventional growth flat of samples, here plants. As shown, the growth flat includes a plurality of samples that are contained in an array of containers, here pots, that are arranged in a container spatial relationship, here four rows of eight columns. The plants possess characteristics that change over time. Data is collected at various points in time, concerning various characteristics of the plants. As shown in FIG. 1, each plant may be identified by a bar code or other indicium that is associated with the corresponding container. Often, data is collected by looking down at the flat and determining a characteristic, such as whether a plant has started to produce buds, flowers, leaves, the color of leaves, the number of leaves, etc.
It will be understood that many other types of containers, such as nutrient plates, may be used in plant research. Moreover, in other organism research, other containers, such as test tubes, petri dishes and the like may be used. However, these research efforts all may be characterized as including a plurality of samples that possess characteristics that change over time, the samples being contained in an array of containers that are arranged in a container spatial relationship.
Large numbers of arrays of containers may be stored in a hierarchical organization that includes, for example, buildings, rooms in a building, racks in a room, shelves in a rack, shelf positions in a shelf, flats in a shelf position, and pots in an array in a flat. Thus, each flat may be identified uniquely by its unique position in the hierarchy. This hierarchy may be used to store data in a database system, such as an SQL*GT database system, marketed by Perkin-Elmer Inc. In such a system, the building, room, rack, shelf and position may be modeled as locations, and may be referenced hierarchically to one another. The flat may be modeled as a two-dimensional container, which also may be referred to as a “plate” in the SQL*GT system. The pots may be modeled as samples. It will be understood, however, that many other database systems may be used to store data about a plurality of samples that are contained in an array of containers, and that possess characteristics that change over time.
Accordingly, although the data that has been collected can be efficiently stored in a database, there still may be a need for methods, systems and computer program products that can allow efficient data collection and efficient scheduling of data collection.